Modelling health-climate interaction

We are pleased to announce a highly competitive PhD opportunity in statistics with a focus on advanced statistical modelling of health and climate data. This project is ideal for candidates eager to contribute to the theoretical development of statistical modelling of spatio-temporal data, with a particular emphasis to understand how climate impacts health outcomes.

Data structures involve high dimensional time-series, spatially linked health data, and model classes include advanced regression modelling, empirical study of covariance structure of dependent time series, etc.

These can then be used, for example, to assess the impact of climate sensitive interventions. Multiple examples exist, where control programmes for diseases that are sensitive to rainfall or temperature need to account of climate when assessing the impact of their interventions.

Key Research Areas

The successful candidate will develop advanced modelling framework for capturing interactions between health and climate data.

Since choice of spatial and temporal scales can influence interpretation, this project aims to develop understanding of the scale and variability in such data.

Different aspects of climate affect different health outcomes both directly and, indirectly, via a combination of biological, environmental and socioeconomic factors.

Aim is to analyse and model climate-health interaction using, empirical statistical models. The scope of the project includes capture/develop the spatial and temporal distribution of disease in relation to climatic and environmental drivers.

Thus, this project gives opportunity to a researcher to work on building a multiscale, multi-variate and multi-disciplinary approach to study climate-health interface.

We are pleased to announce a highly competitive PhD opportunity in statistics with a focus on advanced statistical modelling of health and climate data. This project is ideal for candidates eager to contribute to the theoretical development of statistical modelling of spatio-temporal data, with a particular emphasis to understand how climate impacts health outcomes.

Data structures involve high dimensional time-series, spatially linked health data, and model classes include advanced regression modelling, empirical study of covariance structure of dependent time series, etc.

These can then be used, for example, to assess the impact of climate sensitive interventions. Multiple examples exist, where control programmes for diseases that are sensitive to rainfall or temperature need to account of climate when assessing the impact of their interventions.

Key Research Areas

The successful candidate will develop advanced modelling framework for capturing interactions between health and climate data.

Since choice of spatial and temporal scales can influence interpretation, this project aims to develop understanding of the scale and variability in such data.

Different aspects of climate affect different health outcomes both directly and, indirectly, via a combination of biological, environmental and socioeconomic factors.

Aim is to analyse and model climate-health interaction using, empirical statistical models. The scope of the project includes capture/develop the spatial and temporal distribution of disease in relation to climatic and environmental drivers.

Thus, this project gives opportunity to a researcher to work on building a multiscale, multi-variate and multi-disciplinary approach to study climate-health interface.

What We Offer

1. An opportunity to conduct pioneering research with real-world applications.

2. Access to state-of-the-art facilities and resources.

3. A supportive and collaborative research environment.

4. Expert guidance and mentorship from leading professionals in the field.

5. Join us on a transformative journey where your passion for statistics, mathematics, and high-dimensional data analysis will shape the future of health research.

The project is a part of ongoing collaboration between researchers from University of Manchester (UK), Indian Statistical Institute (India).

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

• MSc in Statistics, Mathematics, or allied areas (with a substantial background in statistics).
• Skill in R programming (substantiated by curriculum taken and/or projects).
• Proficiency in oral & written communication in English.

Desired:

• Evidence of interest in research
• Experience in (statistical) modelling and analysis of spatio-temporal data.

Before you apply

We strongly recommend that you contact the supervisors for this project before you apply.

How to apply

Apply online through our website: https://uom.link/pgr-apply-fap

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).